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Record W3151682906 · doi:10.1155/2021/5551633

Change of Tensile Properties with Aging Time and Temperature in Al‐Si‐Cu‐Mg 354 Cast Alloys with/without Minor Addition of Ni and/or Zr

2021· article· en· W3151682906 on OpenAlexaff
J. Hernández-Sandoval, M. H. Abdelaziz, E. A. Elsharkawi, A. M. Samuel, F. H. Samuel

Bibliographic record

VenueAdvances in Materials Science and Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsCégep de ChicoutimiUniversité du Québec à ChicoutimiSaint Mary's University
Fundersnot available
KeywordsMaterials scienceAlloyIntermetallicUltimate tensile strengthLiquidusDuctility (Earth science)MicrostructureMetallurgyPrecipitationZirconiumAluminiumPhase (matter)Creep

Abstract

fetched live from OpenAlex

The principal aim of the present research work was to investigate the effects of minor additions of nickel and zirconium on the strength of cast aluminum alloy 354 at room temperature (25°C). A decrease in tensile properties of ∼10% with the addition of 0.4 wt.% nickel is attributed to a nickel‐copper reaction which interferes with the formation of Al 2 Cu precipitates. The platelet‐like phases (Al,Si) 3 (Zr,Ni,Fe) and (Al,Si) 3 (Zr,Ti) are the main features observed in the microstructures of the tensile samples of alloys with Zr additions. The reduction in mechanical properties is due to the increase in the percentage of intermetallic phases formed during solidification; such particles would act as stress concentrators, decreasing the alloy ductility. The main effect of Zr addition lies in a significant reduction in the alloy grain size ∼40%, rather than an increase in the mechanical properties. Quality index charts could be used in assessing the effects of the Ni and Zr additions to the base alloy, as well as evaluating the heat treatment relationships to the alloy tensile properties, in particular when the system shows multiple precipitation reactions. Due to the high liquidus temperature of the Al‐Zr binary phase diagram, addition of Zr beyond 0.2% is not recommended to avoid undissolved Zr.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.192
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueAdvances in Materials Science and EngineeringSame topicAluminum Alloy Microstructure PropertiesFrench-language works237,207